Modelling of storage processes in
TIMES-PanEU
Julia Welsch, PD Dr. Markus Blesl
Institute for Energy Economics and the Rational Use of Energy
University of Stuttgart
Germany
Copenhagen, 18.11.2014
Structure
1
Introduction
2
The TIMES-PanEU energy system model
3
Modelling of storage processes in TIMES-PanEU
4
Exemplary results
5
Conclusion and outlook
Julia Welsch
18.11.2014
2
Structure
1
Introduction
2
The TIMES-PanEU energy system model
3
Modelling of storage processes in TIMES-PanEU
4
Exemplary results
5
Conclusion and outlook
Julia Welsch
18.11.2014
3
Motivation and objective
Motivation:
●
Political included expansion of electricity generation from renewable energies in
Germany
●
In consequence increasingly fluctuating feeding of electricity from wind- and
photovoltaic systems
 Thus there will be occur to an increasing degree negative and fluctuating
residual loads in the future
 Storage of excess electricity or curtailment
 Versatile possibilities for using excess electricity
Objective:
●
Determination of optimal configuration of storage- and Power-to-X-technologies
(for Germany) under minimization of total system costs
●
Analysis of interactions between energy supply and energy demand with use of
Power-to-X
Julia Welsch
18.11.2014
4
Structure
1
Introduction
2
The TIMES-PanEU energy system model
3
Modelling of storage processes in TIMES-PanEU
4
Exemplary results
5
Conclusion and outlook
Julia Welsch
18.11.2014
5
The TIMES-PanEU energy system model
●
Linear optimization model
●
30 regions (EU-28 + Norway, Switzerland)
●
Time horizon: 2010 – 2050
●
Mapping of the whole energy system:
i.
Energy supply (electricity, heat, gas)
ii. Energy demand, divided into sectors:
1. Residential sector
2. Commercial sector
3. Agriculture
4. Industry
5. Transport
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General structure of TIMES-PanEU
GDP
Coal processing
Domestic
sources
Process energy
Industry
Heating area
Refineries
Power plants,
Storage and
Transportation
Commercial and
tertiary sector
Light
Households
CHP plants
and district
heat networks
Transportation
Person
kilometers
Freight
kilometers
Gas network
Julia Welsch
Communication
Power
Imports
Primary energy
Population
Demands
Energy prices, Resource availability
Cost and emissions balance
Final energy
Demand services
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Temporal resolution
Germany
Rest of Europe
●
●
12 time segments
●
Discontinuous temporal
resolution
224 time segments (One week
per season, three-hourly)
●
Continuous temporal resolution
for mapping storage processes
Milestoneyear
Annual
Milestoneyear
R
S
F
Season
W
WS56
WP
WN
WD
FP
FN
FD
SP
SN
SD
RP
RN
Weekly
RD
…
WS1
W
FS56
…
FS1
F
SS56
…
SS1
S
RS56
…
RS1
R
Daynite
 Coupling of timeslice trees for modelling trade processes with parameter IRE_TSCVT
 Integral optimization
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Examplary demand services
RCA
Residential
Space Heat
Space Cool
Commercial
Water Heat
Other
Agriculture
Space Heat
Space Cool
Water Heat
Other
Single Rural
Single Rural
Single Rural
Lighting
Small
Small
Small
Lighting
Single Urban
Single Urban
Single Urban
Cooking
Large
Large
Large
Cooking
Multi
Multi
Multi
Refrigeration
Refrigeration
Cloth
Washing
Public
Lighting
Cloth Drying
Other Electric
Dish
Washing
Other Energy
Other Electric
Other Energy
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18.11.2014
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Residential Other
RCA
Residential
Space Heat
Space Cool
Commercial
Water Heat
Other
Agriculture
Space Heat
Space Cool
Water Heat
Other
Single Rural
Single Rural
Single Rural
Lighting
Small
Small
Small
Lighting
Single Urban
Single Urban
Single Urban
Cooking
Large
Large
Large
Cooking
Multi
Multi
Multi
Refrigeration
Refrigeration
Cloth
Washing
Public
Lighting
Cloth Drying
Other Electric
Dish
Washing
Other Energy
Other Electric
Other Energy
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18.11.2014
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Residential Other
Power
30
GW
25
Other
20
Lighting
15
Cooking
Dish Washing
10
Cloth Drying
Power
5
Cloth Washing
25
Refrigeration
0
Other
GW
20
Lighting
Cooking
15
Dish Washing
10
Cloth Drying
Cloth Washing
5
Refrigeration
0
Julia Welsch
18.11.2014
11
Structure
1
Introduction
2
The TIMES-PanEU energy system model
3
Modelling of storage processes in TIMES-PanEU
4
Exemplary results
5
Conclusion and outlook
Julia Welsch
16.11.2014
12
Modelling of storage processes in TIMES-PanEU
Storage IN
Storage
Storage OUT
𝑃𝑅𝐶_𝐴𝐶𝑇𝑈𝑁𝑇
𝑃𝐽
𝑃𝐽
𝑃𝐽
𝑃𝑅𝐶_𝐶𝐴𝑃𝑈𝑁𝑇
𝐺𝑊
𝑃𝐽
𝐺𝑊
𝑃𝑅𝐶_𝐶𝐴𝑃𝐴𝐶𝑇
8760 𝐺𝑊ℎ
𝐺𝑊
𝑠
𝑃𝑊
8760 ℎ ∙ 3600 ∙ 10−6 𝐺𝑊 ∙ 𝐺𝑊
ℎ
=
𝐺𝑊
𝑃𝐽
= 31,536
𝐺𝑊
1
𝑃𝐽
𝑃𝐽
31,536
𝑃𝐽
𝐺𝑊
𝑃𝑅𝐶− 𝐴𝐶𝑇𝑈𝑁𝑇: 𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑜𝑓 𝑎 𝑝𝑟𝑜𝑐𝑒𝑠𝑠
𝑃𝑅𝐶− 𝐶𝐴𝑃𝑈𝑁𝑇: 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑜𝑓 𝑎 𝑝𝑟𝑜𝑐𝑒𝑠𝑠
𝑃𝑅𝐶− 𝐶𝐴𝑃𝐴𝐶𝑇: 𝑅𝑎𝑡𝑖𝑜 𝑓𝑟𝑜𝑚 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑎𝑛𝑑 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦
 Storage power and storage capacity are endogenous results of modeling
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Modelling of storage processes in TIMES
Two different types of storage processes in TIMES:
●
Inter-Period Storage: Storage between periods (Store in and store out with
constant power over the whole period)
●
Timeslice Storage: Storage between time segments within a period (in
accordance with the definition of the storage level)
●
Generalized timeslice storage: Combination of timeslice storages with different
timeslice levels, STS or STK
Storage
Milestoneyear
Julia Welsch
W
Night
Weekend
Day
Night
Weekday
Day
Night
Weekend
Day
Night
Weekday
Day
Weekend
Night
Night
Weekday
Day
Day
IN
Night
Weekend
Night
Day
Weekday
F
S
Day
R
OUT
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Timeslice Storage
General simplified equations (∀ 𝑟, 𝑣, 𝑡, 𝑝, 𝑐, 𝑡𝑠) :
1.
Time overall equation EQ_STGTSS: 𝑉𝐴𝑅− 𝐴𝐶𝑇 𝑟, 𝑣, 𝑡, 𝑝, 𝑡𝑠 = 𝑉𝐴𝑅− 𝐴𝐶𝑇 𝑟, 𝑣, 𝑡, 𝑝, 𝑡𝑠 − 1 +
𝑉𝐴𝑅− 𝑆𝐼𝑁 𝑟, 𝑣, 𝑡, 𝑝, 𝑐, 𝑡𝑠 − 1 – 𝑉𝐴𝑅− 𝑆𝑂𝑈𝑇 𝑟, 𝑣, 𝑡, 𝑝, 𝑐, 𝑡𝑠 − 1
2.
Whole storage capacity is available in every timeslice EQL_CAPACT:
𝑉𝐴𝑅− 𝐴𝐶𝑇 𝑟, 𝑣, 𝑡, 𝑝, 𝑡𝑠 ∙
𝑡
𝑣=𝐵𝑎𝑠𝑒𝑦𝑒𝑎𝑟
1
𝑅𝑆− 𝑆𝑇𝐺𝑃𝑅𝐷(𝑟,𝑡𝑠)
𝑉𝐴𝑅− 𝑁𝐶𝐴𝑃 𝑟, 𝑣, 𝑝
r: Region
v: Year of commissioning
t: Current period
p: Process
c: Commodity
ts: timeslices of storage level
VAR_ACT: Storage content at the beginning of ts
VAR_NCAP: New installed capacity
NCAP_PASTI: Stock
PRC_CAPACT = 1
Julia Welsch
≤
+ 𝑁𝐶𝐴𝑃− 𝑃𝐴𝑆𝑇𝐼(𝑟, 𝑣, 𝑝) ∙ 𝑃𝑅𝐶− 𝐶𝐴𝑃𝐴𝐶𝑇(𝑟, 𝑝)
Storage Level
𝑹𝑺− 𝑺𝑻𝑮𝑷𝑹𝑫 𝒓, 𝒕𝒔
Season/
Annual
1
Weekly
Daynite
8760
∙ 𝐺 𝑌𝑅𝐹𝑅(𝑟, 𝑥)
24 ∙ 7 −
365 ∙ 𝐺− 𝑌𝑅𝐹𝑅(𝑟, 𝑥)
(x is directly
upstream, defined
node of weekly)
(x is directly
upstream, defined
node of daynite)
18.11.2014
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Structure
1
Introduction
2
The TIMES-PanEU energy system model
3
Modelling of storage processes in TIMES-PanEU
4
Exemplary results
5
Conclusion and outlook
Julia Welsch
18.11.2014
16
Operation of electricity storage in the year
2050 without Curtailment (only Germany)
Power 60
60
Summer 2050
Spring 2050
GW
40
40
20
20
0
0
-20
-20
60
60
Fall 2050
40
40
20
20
0
0
-20
-20
Winter 2050
Residual load
Storage power
 Store in times of low or negative residual load (electricity price low)
 Store out in times of high residual load (electricity price high)
Julia Welsch
18.11.2014
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Operation of electricity storage in the year
2050 with Curtailment (only Germany)
Power
Fall 2050 without curtailment
Fall 2050 with curtailment
60
60
GW
50
50
40
40
30
30
20
20
10
10
0
0
-10
-10
-20
-20
Residual load
Storage power
 Lower storage capacity than in scenario without curtailment
 Lower total system costs in scenario with curtailment
Julia Welsch
18.11.2014
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Structure
1
Introduction
2
The TIMES-PanEU energy system model
3
Modelling of storage processes in TIMES-PanEU
4
Exemplary results
5
Conclusion and outlook
Julia Welsch
18.11.2014
19
Conclusion and outlook
Conclusion:
● Need of more electricity storage with increasingly power input of fluctuating
renewable energies
● Objective value difference ca. 35 Billions Euro
 Possibility of curtailment leads to a lower electricity storage capacity and lower
total system costs
Outlook:
● Analysis of Power-to-Heat, Power-to-Gas and other electricity storages
(compressed air, battery)
● Reserve power
● Differenet scenarios
● Sensitivity analysis
Julia Welsch
18.11.2014
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Thank you!
[email protected]
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18.11.2014
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Backup
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18.11.2014
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Modeling of storage processes in TIMES-PanEU
Energy storage
Transformation processes
Electricity storage
Pumped storage
Compressed air storage
Battery storage
Heat storage
Power-to-Heat
Hot water storage
Gas storage
Julia Welsch
Heater
Power-to-Gas
Hydrogen storage
Elektrolysis
Natural gas storage
Fuel cell
Natural gas grid
Methanation
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